51 citations · 142 across the 15 of their papers we have counts for
16 papers
Statistical constraints on climate model parameters using a scalable cloud-based inference framework
James Carzon, Bruno R. de Abreu, Leighton Regayre +5
Atmospheric aerosols influence the Earth's climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate…
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods
Julia Walchessen, Amanda Lenzi, Mikael Kuusela
In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to rea…
Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport
Tudor Manole, Patrick Bryant, John Alison +2
We study the problem of data-driven background estimation, arising in the search of physics signals predicted by the Standard Model at the Large Hadron Collider. Our work is motiva…
Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
Luca Masserano, Tommaso Dorigo, Rafael Izbicki +2
Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especia…
Uncertainty quantification for wide-bin unfolding: one-at-a-time strict bounds and prior-optimized confidence intervals
Michael Stanley, Pratik Patil, Mikael Kuusela
Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level spectrum from smeared detector-level data. For computational and practical reaso…
Spatio-temporal Local Interpolation of Global Ocean Heat Transport using Argo Floats: A Debiased Latent Gaussian Process Approach
Beomjo Park, Mikael Kuusela, Donata Giglio +1
The world ocean plays a key role in redistributing heat in the climate system and hence in regulating Earth's climate. Yet statistical analysis of ocean heat transport suffers from…
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
Purvasha Chakravarti, Mikael Kuusela, Jing Lei +1
A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this paper, we aim to search for new signals that ap…
Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones
Addison J. Hu, Mikael Kuusela, Ann B. Lee +2
Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface…
Objective frequentist uncertainty quantification for atmospheric CO retrievals
Pratik Patil, Mikael Kuusela, Jonathan Hobbs
The steadily increasing amount of atmospheric carbon dioxide (CO) is affecting the global climate system and threatening the long-term sustainability of Earth's ecosystem. In o…
Locally stationary spatio-temporal interpolation of Argo profiling float data
Mikael Kuusela, Michael L. Stein
Argo floats measure seawater temperature and salinity in the upper 2,000 m of the global ocean. Statistical analysis of the resulting spatio-temporal dataset is challenging due to…
Shape-constrained uncertainty quantification in unfolding steeply falling elementary particle spectra
Mikael Kuusela, Philip B. Stark
The high energy physics unfolding problem is an important statistical inverse problem in data analysis at the Large Hadron Collider (LHC) at CERN. The goal of unfolding is to make…
Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification
Mikael Kuusela, Victor M. Panaretos
We consider the high energy physics unfolding problem where the goal is to estimate the spectrum of elementary particles given observations distorted by the limited resolution of a…
Empirical Bayes unfolding of elementary particle spectra at the Large Hadron Collider
Mikael Kuusela, Victor M. Panaretos
We consider the so-called unfolding problem in experimental high energy physics, where the goal is to estimate the true spectrum of elementary particles given observations distorte…
Semi-Supervised Anomaly Detection - Towards Model-Independent Searches of New Physics
Mikael Kuusela, Tommi Vatanen, Eric Malmi +3
Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and ba…
Soft Classification of Diffractive Interactions at the LHC
Mikael Kuusela, Eric Malmi, Risto Orava +1
Multivariate machine learning techniques provide an alternative to the rapidity gap method for event-by-event identification and classification of diffraction in hadron-hadron coll…
Multivariate Techniques for Identifying Diffractive Interactions at the LHC
Mikael Kuusela, Jerry W. Lamsa, Eric Malmi +2
Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower…